Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations119390
Missing cells129425
Missing cells (%)3.4%
Duplicate rows8171
Duplicate rows (%)6.8%
Total size in memory29.1 MiB
Average record size in memory256.0 B

Variable types

Categorical16
Numeric14
Text1
DateTime1

Alerts

Dataset has 8171 (6.8%) duplicate rowsDuplicates
agent is highly overall correlated with hotelHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
children is highly imbalanced (80.7%)Imbalance
babies is highly imbalanced (97.2%)Imbalance
meal is highly imbalanced (53.5%)Imbalance
distribution_channel is highly imbalanced (63.2%)Imbalance
is_repeated_guest is highly imbalanced (79.6%)Imbalance
reserved_room_type is highly imbalanced (58.3%)Imbalance
assigned_room_type is highly imbalanced (51.4%)Imbalance
deposit_type is highly imbalanced (65.3%)Imbalance
customer_type is highly imbalanced (50.6%)Imbalance
required_car_parking_spaces is highly imbalanced (85.4%)Imbalance
agent has 16340 (13.7%) missing valuesMissing
company has 112593 (94.3%) missing valuesMissing
previous_cancellations is highly skewed (γ1 = 24.45804872)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53979995)Skewed
lead_time has 6345 (5.3%) zerosZeros
stays_in_weekend_nights has 51998 (43.6%) zerosZeros
stays_in_week_nights has 7645 (6.4%) zerosZeros
previous_cancellations has 112906 (94.6%) zerosZeros
previous_bookings_not_canceled has 115770 (97.0%) zerosZeros
booking_changes has 101314 (84.9%) zerosZeros
days_in_waiting_list has 115692 (96.9%) zerosZeros
adr has 1959 (1.6%) zerosZeros
total_of_special_requests has 70318 (58.9%) zerosZeros

Reproduction

Analysis started2024-12-09 13:21:27.666989
Analysis finished2024-12-09 13:22:13.769250
Duration46.1 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

hotel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
City Hotel
79330 
Resort Hotel
40060 

Length

Max length12
Median length10
Mean length10.671078
Min length10

Characters and Unicode

Total characters1274020
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 79330
66.4%
Resort Hotel 40060
33.6%

Length

2024-12-09T14:22:13.925807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:14.081425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hotel 119390
50.0%
city 79330
33.2%
resort 40060
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1274020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1274020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1274020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 238780
18.7%
o 159450
12.5%
e 159450
12.5%
119390
9.4%
H 119390
9.4%
l 119390
9.4%
C 79330
 
6.2%
i 79330
 
6.2%
y 79330
 
6.2%
R 40060
 
3.1%
Other values (2) 80120
 
6.3%

is_canceled
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
75166 
1
44224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Length

2024-12-09T14:22:14.226599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:14.367780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring characters

ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75166
63.0%
1 44224
37.0%

lead_time
Real number (ℝ)

ZEROS 

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.01142
Minimum0
Maximum737
Zeros6345
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:14.535510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3160
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.8631
Coefficient of variation (CV)1.027417
Kurtosis1.6964488
Mean104.01142
Median Absolute Deviation (MAD)60
Skewness1.3465499
Sum12417923
Variance11419.722
MonotonicityNot monotonic
2024-12-09T14:22:14.850228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6345
 
5.3%
1 3460
 
2.9%
2 2069
 
1.7%
3 1816
 
1.5%
4 1715
 
1.4%
5 1565
 
1.3%
6 1445
 
1.2%
7 1331
 
1.1%
8 1138
 
1.0%
12 1079
 
0.9%
Other values (469) 97427
81.6%
ValueCountFrequency (%)
0 6345
5.3%
1 3460
2.9%
2 2069
 
1.7%
3 1816
 
1.5%
4 1715
 
1.4%
5 1565
 
1.3%
6 1445
 
1.2%
7 1331
 
1.1%
8 1138
 
1.0%
9 992
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
2016
56707 
2017
40687 
2015
21996 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters477560
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 56707
47.5%
2017 40687
34.1%
2015 21996
 
18.4%

Length

2024-12-09T14:22:15.056714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:15.209382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 56707
47.5%
2017 40687
34.1%
2015 21996
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 477560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 477560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 477560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 119390
25.0%
0 119390
25.0%
1 119390
25.0%
6 56707
11.9%
7 40687
 
8.5%
5 21996
 
4.6%

arrival_date_month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
August
13877 
July
12661 
May
11791 
October
11160 
April
11089 
Other values (7)
58812 

Length

Max length9
Median length7
Mean length5.9031828
Min length3

Characters and Unicode

Total characters704781
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 13877
11.6%
July 12661
10.6%
May 11791
9.9%
October 11160
9.3%
April 11089
9.3%
June 10939
9.2%
September 10508
8.8%
March 9794
8.2%
February 8068
6.8%
November 6794
5.7%
Other values (2) 12709
10.6%

Length

2024-12-09T14:22:15.364588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 13877
11.6%
july 12661
10.6%
may 11791
9.9%
october 11160
9.3%
april 11089
9.3%
june 10939
9.2%
september 10508
8.8%
march 9794
8.2%
february 8068
6.8%
november 6794
5.7%
Other values (2) 12709
10.6%

Most occurring characters

ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 704781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 704781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 704781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 95619
13.6%
r 78190
 
11.1%
u 65351
 
9.3%
b 43310
 
6.1%
a 41511
 
5.9%
y 38449
 
5.5%
t 35545
 
5.0%
J 29529
 
4.2%
c 27734
 
3.9%
A 24966
 
3.5%
Other values (16) 224577
31.9%

arrival_date_week_number
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.165173
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:15.536231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.605138
Coefficient of variation (CV)0.50083018
Kurtosis-0.98607718
Mean27.165173
Median Absolute Deviation (MAD)11
Skewness-0.010014326
Sum3243250
Variance185.09979
MonotonicityNot monotonic
2024-12-09T14:22:15.720876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 3580
 
3.0%
30 3087
 
2.6%
32 3045
 
2.6%
34 3040
 
2.5%
18 2926
 
2.5%
21 2854
 
2.4%
28 2853
 
2.4%
17 2805
 
2.3%
20 2785
 
2.3%
29 2763
 
2.3%
Other values (43) 89652
75.1%
ValueCountFrequency (%)
1 1047
0.9%
2 1218
1.0%
3 1319
1.1%
4 1487
1.2%
5 1387
1.2%
6 1508
1.3%
7 2109
1.8%
8 2216
1.9%
9 2117
1.8%
10 2149
1.8%
ValueCountFrequency (%)
53 1816
1.5%
52 1195
1.0%
51 933
0.8%
50 1505
1.3%
49 1782
1.5%
48 1504
1.3%
47 1685
1.4%
46 1574
1.3%
45 1941
1.6%
44 2272
1.9%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.798241
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:15.878486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7808295
Coefficient of variation (CV)0.55581058
Kurtosis-1.1871683
Mean15.798241
Median Absolute Deviation (MAD)8
Skewness-0.002000454
Sum1886152
Variance77.102966
MonotonicityNot monotonic
2024-12-09T14:22:16.025720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 4406
 
3.7%
5 4317
 
3.6%
15 4196
 
3.5%
25 4160
 
3.5%
26 4147
 
3.5%
9 4096
 
3.4%
12 4087
 
3.4%
16 4078
 
3.4%
2 4055
 
3.4%
19 4052
 
3.4%
Other values (21) 77796
65.2%
ValueCountFrequency (%)
1 3626
3.0%
2 4055
3.4%
3 3855
3.2%
4 3763
3.2%
5 4317
3.6%
6 3833
3.2%
7 3665
3.1%
8 3921
3.3%
9 4096
3.4%
10 3575
3.0%
ValueCountFrequency (%)
31 2208
1.8%
30 3853
3.2%
29 3580
3.0%
28 3946
3.3%
27 3802
3.2%
26 4147
3.5%
25 4160
3.5%
24 3993
3.3%
23 3616
3.0%
22 3596
3.0%

stays_in_weekend_nights
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92759863
Minimum0
Maximum19
Zeros51998
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:16.173861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99861349
Coefficient of variation (CV)1.0765578
Kurtosis7.1740661
Mean0.92759863
Median Absolute Deviation (MAD)1
Skewness1.3800464
Sum110746
Variance0.99722891
MonotonicityNot monotonic
2024-12-09T14:22:16.314552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 51998
43.6%
2 33308
27.9%
1 30626
25.7%
4 1855
 
1.6%
3 1259
 
1.1%
6 153
 
0.1%
5 79
 
0.1%
8 60
 
0.1%
7 19
 
< 0.1%
9 11
 
< 0.1%
Other values (7) 22
 
< 0.1%
ValueCountFrequency (%)
0 51998
43.6%
1 30626
25.7%
2 33308
27.9%
3 1259
 
1.1%
4 1855
 
1.6%
5 79
 
0.1%
6 153
 
0.1%
7 19
 
< 0.1%
8 60
 
0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 11
 
< 0.1%
8 60
0.1%
7 19
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5003015
Minimum0
Maximum50
Zeros7645
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:16.462746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9082856
Coefficient of variation (CV)0.76322219
Kurtosis24.284555
Mean2.5003015
Median Absolute Deviation (MAD)1
Skewness2.8622492
Sum298511
Variance3.641554
MonotonicityNot monotonic
2024-12-09T14:22:16.620467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 33684
28.2%
1 30310
25.4%
3 22258
18.6%
5 11077
 
9.3%
4 9563
 
8.0%
0 7645
 
6.4%
6 1499
 
1.3%
10 1036
 
0.9%
7 1029
 
0.9%
8 656
 
0.5%
Other values (25) 633
 
0.5%
ValueCountFrequency (%)
0 7645
 
6.4%
1 30310
25.4%
2 33684
28.2%
3 22258
18.6%
4 9563
 
8.0%
5 11077
 
9.3%
6 1499
 
1.3%
7 1029
 
0.9%
8 656
 
0.5%
9 231
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 5
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8564034
Minimum0
Maximum55
Zeros403
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:16.751734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.579261
Coefficient of variation (CV)0.31203401
Kurtosis1352.1151
Mean1.8564034
Median Absolute Deviation (MAD)0
Skewness18.317805
Sum221636
Variance0.3355433
MonotonicityNot monotonic
2024-12-09T14:22:16.884402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 89680
75.1%
1 23027
 
19.3%
3 6202
 
5.2%
0 403
 
0.3%
4 62
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 403
 
0.3%
1 23027
 
19.3%
2 89680
75.1%
3 6202
 
5.2%
4 62
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 62
0.1%

children
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size932.9 KiB
0.0
110796 
1.0
 
4861
2.0
 
3652
3.0
 
76
10.0
 
1

Length

Max length4
Median length3
Mean length3.0000084
Min length3

Characters and Unicode

Total characters358159
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110796
92.8%
1.0 4861
 
4.1%
2.0 3652
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2024-12-09T14:22:17.038507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:17.176720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110796
92.8%
1.0 4861
 
4.1%
2.0 3652
 
3.1%
3.0 76
 
0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 230183
64.3%
. 119386
33.3%
1 4862
 
1.4%
2 3652
 
1.0%
3 76
 
< 0.1%

babies
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
118473 
1
 
900
2
 
15
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters119391
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118473
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Length

2024-12-09T14:22:17.341447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:17.506133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 118473
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118474
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

meal
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
BB
92310 
HB
14463 
SC
10650 
Undefined
 
1169
FB
 
798

Length

Max length9
Median length2
Mean length2.0685401
Min length2

Characters and Unicode

Total characters246963
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 92310
77.3%
HB 14463
 
12.1%
SC 10650
 
8.9%
Undefined 1169
 
1.0%
FB 798
 
0.7%

Length

2024-12-09T14:22:17.750262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:17.945854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
bb 92310
77.3%
hb 14463
 
12.1%
sc 10650
 
8.9%
undefined 1169
 
1.0%
fb 798
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 199881
80.9%
H 14463
 
5.9%
S 10650
 
4.3%
C 10650
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%
Distinct177
Distinct (%)0.1%
Missing488
Missing (%)0.4%
Memory size932.9 KiB
2024-12-09T14:22:18.260646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9892432
Min length2

Characters and Unicode

Total characters355427
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 48590
40.9%
gbr 12129
 
10.2%
fra 10415
 
8.8%
esp 8568
 
7.2%
deu 7287
 
6.1%
ita 3766
 
3.2%
irl 3375
 
2.8%
bel 2342
 
2.0%
bra 2224
 
1.9%
nld 2104
 
1.8%
Other values (167) 18102
 
15.2%
2024-12-09T14:22:18.741180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 355427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 355427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 355427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 80804
22.7%
P 58506
16.5%
T 54263
15.3%
A 21627
 
6.1%
E 21538
 
6.1%
B 17051
 
4.8%
S 13931
 
3.9%
U 13293
 
3.7%
G 13130
 
3.7%
F 10956
 
3.1%
Other values (16) 50328
14.2%

market_segment
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Online TA
56477 
Offline TA/TO
24219 
Groups
19811 
Direct
12606 
Corporate
 
5295
Other values (3)
 
982

Length

Max length13
Median length9
Mean length9.0197671
Min length6

Characters and Unicode

Total characters1076870
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 56477
47.3%
Offline TA/TO 24219
20.3%
Groups 19811
 
16.6%
Direct 12606
 
10.6%
Corporate 5295
 
4.4%
Complementary 743
 
0.6%
Aviation 237
 
0.2%
Undefined 2
 
< 0.1%

Length

2024-12-09T14:22:18.927820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:19.117421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
online 56477
28.2%
ta 56477
28.2%
offline 24219
12.1%
ta/to 24219
12.1%
groups 19811
 
9.9%
direct 12606
 
6.3%
corporate 5295
 
2.6%
complementary 743
 
0.4%
aviation 237
 
0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1076870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1076870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1076870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 138157
12.8%
O 104915
9.7%
T 104915
9.7%
e 100087
9.3%
i 93778
8.7%
l 81439
7.6%
A 80933
7.5%
80696
7.5%
f 48440
 
4.5%
r 43750
 
4.1%
Other values (16) 199760
18.6%

distribution_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
TA/TO
97870 
Direct
14645 
Corporate
 
6677
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3433035
Min length3

Characters and Unicode

Total characters637937
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 97870
82.0%
Direct 14645
 
12.3%
Corporate 6677
 
5.6%
GDS 193
 
0.2%
Undefined 5
 
< 0.1%

Length

2024-12-09T14:22:19.306096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:19.505588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 97870
82.0%
direct 14645
 
12.3%
corporate 6677
 
5.6%
gds 193
 
0.2%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 637937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 637937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 637937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 195740
30.7%
/ 97870
15.3%
O 97870
15.3%
A 97870
15.3%
r 27999
 
4.4%
e 21332
 
3.3%
t 21322
 
3.3%
D 14838
 
2.3%
i 14650
 
2.3%
c 14645
 
2.3%
Other values (10) 33801
 
5.3%

is_repeated_guest
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
115580 
1
 
3810

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Length

2024-12-09T14:22:19.677652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:19.858210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 115580
96.8%
1 3810
 
3.2%

previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087117849
Minimum0
Maximum26
Zeros112906
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:19.978410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84433638
Coefficient of variation (CV)9.6918874
Kurtosis674.07369
Mean0.087117849
Median Absolute Deviation (MAD)0
Skewness24.458049
Sum10401
Variance0.71290393
MonotonicityNot monotonic
2024-12-09T14:22:20.119139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 112906
94.6%
1 6051
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
24 48
 
< 0.1%
11 35
 
< 0.1%
4 31
 
< 0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
6 22
 
< 0.1%
Other values (5) 65
 
0.1%
ValueCountFrequency (%)
0 112906
94.6%
1 6051
 
5.1%
2 116
 
0.1%
3 65
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 35
 
< 0.1%
13 12
 
< 0.1%
14 14
 
< 0.1%
ValueCountFrequency (%)
26 26
< 0.1%
25 25
< 0.1%
24 48
< 0.1%
21 1
 
< 0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
13 12
 
< 0.1%
11 35
< 0.1%
6 22
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

SKEWED  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13709691
Minimum0
Maximum72
Zeros115770
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:20.373683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4974368
Coefficient of variation (CV)10.92247
Kurtosis767.24521
Mean0.13709691
Median Absolute Deviation (MAD)0
Skewness23.5398
Sum16368
Variance2.2423171
MonotonicityNot monotonic
2024-12-09T14:22:20.552240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115770
97.0%
1 1542
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
Other values (63) 422
 
0.4%
ValueCountFrequency (%)
0 115770
97.0%
1 1542
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 115
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 60
 
0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
A
85994 
D
19201 
E
 
6535
F
 
2897
G
 
2094
Other values (5)
 
2669

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Length

2024-12-09T14:22:20.728826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:20.879953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 85994
72.0%
d 19201
 
16.1%
e 6535
 
5.5%
f 2897
 
2.4%
g 2094
 
1.8%
b 1118
 
0.9%
c 932
 
0.8%
h 601
 
0.5%
p 12
 
< 0.1%
l 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%

assigned_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
A
74053 
D
25322 
E
7806 
F
 
3751
G
 
2553
Other values (7)
 
5905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Length

2024-12-09T14:22:21.058618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 74053
62.0%
d 25322
 
21.2%
e 7806
 
6.5%
f 3751
 
3.1%
g 2553
 
2.1%
c 2375
 
2.0%
b 2163
 
1.8%
h 712
 
0.6%
i 363
 
0.3%
k 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2) 13
 
< 0.1%

booking_changes
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22112405
Minimum0
Maximum21
Zeros101314
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:21.200348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65230557
Coefficient of variation (CV)2.9499531
Kurtosis79.393605
Mean0.22112405
Median Absolute Deviation (MAD)0
Skewness6.0002701
Sum26400
Variance0.42550256
MonotonicityNot monotonic
2024-12-09T14:22:21.343513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 101314
84.9%
1 12701
 
10.6%
2 3805
 
3.2%
3 927
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
Other values (11) 30
 
< 0.1%
ValueCountFrequency (%)
0 101314
84.9%
1 12701
 
10.6%
2 3805
 
3.2%
3 927
 
0.8%
4 376
 
0.3%
5 118
 
0.1%
6 63
 
0.1%
7 31
 
< 0.1%
8 17
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%

deposit_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
No Deposit
104641 
Non Refund
14587 
Refundable
 
162

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1193900
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 104641
87.6%
Non Refund 14587
 
12.2%
Refundable 162
 
0.1%

Length

2024-12-09T14:22:21.495285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:21.655345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 104641
43.9%
deposit 104641
43.9%
non 14587
 
6.1%
refund 14587
 
6.1%
refundable 162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1193900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1193900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1193900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 223869
18.8%
e 119552
10.0%
N 119228
10.0%
119228
10.0%
s 104641
8.8%
i 104641
8.8%
t 104641
8.8%
p 104641
8.8%
D 104641
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

agent
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct333
Distinct (%)0.3%
Missing16340
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean86.693382
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:21.843115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)220

Descriptive statistics

Standard deviation110.77455
Coefficient of variation (CV)1.277774
Kurtosis-0.0071795649
Mean86.693382
Median Absolute Deviation (MAD)13
Skewness1.0893856
Sum8933753
Variance12271
MonotonicityNot monotonic
2024-12-09T14:22:22.073643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 31961
26.8%
240 13922
11.7%
1 7191
 
6.0%
14 3640
 
3.0%
7 3539
 
3.0%
6 3290
 
2.8%
250 2870
 
2.4%
241 1721
 
1.4%
28 1666
 
1.4%
8 1514
 
1.3%
Other values (323) 31736
26.6%
(Missing) 16340
13.7%
ValueCountFrequency (%)
1 7191
 
6.0%
2 162
 
0.1%
3 1336
 
1.1%
4 47
 
< 0.1%
5 330
 
0.3%
6 3290
 
2.8%
7 3539
 
3.0%
8 1514
 
1.3%
9 31961
26.8%
10 260
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
< 0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

company
Real number (ℝ)

MISSING 

Distinct352
Distinct (%)5.2%
Missing112593
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean189.26674
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:22.293587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q162
median179
Q3270
95-th percentile435
Maximum543
Range537
Interquartile range (IQR)208

Descriptive statistics

Standard deviation131.65501
Coefficient of variation (CV)0.69560567
Kurtosis-0.49079521
Mean189.26674
Median Absolute Deviation (MAD)111
Skewness0.60159967
Sum1286446
Variance17333.043
MonotonicityNot monotonic
2024-12-09T14:22:22.508091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 927
 
0.8%
223 784
 
0.7%
67 267
 
0.2%
45 250
 
0.2%
153 215
 
0.2%
174 149
 
0.1%
219 141
 
0.1%
281 138
 
0.1%
154 133
 
0.1%
405 119
 
0.1%
Other values (342) 3674
 
3.1%
(Missing) 112593
94.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
8 1
 
< 0.1%
9 37
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 14
 
< 0.1%
14 9
 
< 0.1%
16 5
 
< 0.1%
18 1
 
< 0.1%
20 50
< 0.1%
ValueCountFrequency (%)
543 2
 
< 0.1%
541 1
 
< 0.1%
539 2
 
< 0.1%
534 2
 
< 0.1%
531 1
 
< 0.1%
530 5
 
< 0.1%
528 2
 
< 0.1%
525 15
< 0.1%
523 19
< 0.1%
521 7
 
< 0.1%

days_in_waiting_list
Real number (ℝ)

ZEROS 

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3211492
Minimum0
Maximum391
Zeros115692
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:22.715688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.594721
Coefficient of variation (CV)7.5801767
Kurtosis186.79307
Mean2.3211492
Median Absolute Deviation (MAD)0
Skewness11.944353
Sum277122
Variance309.5742
MonotonicityNot monotonic
2024-12-09T14:22:22.918148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115692
96.9%
39 227
 
0.2%
58 164
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
35 96
 
0.1%
46 94
 
0.1%
69 89
 
0.1%
63 83
 
0.1%
87 80
 
0.1%
Other values (118) 2597
 
2.2%
ValueCountFrequency (%)
0 115692
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 16
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
< 0.1%
224 10
 
< 0.1%
223 61
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Transient
89613 
Transient-Party
25124 
Contract
 
4076
Group
 
577

Length

Max length15
Median length9
Mean length10.209146
Min length5

Characters and Unicode

Total characters1218870
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 89613
75.1%
Transient-Party 25124
 
21.0%
Contract 4076
 
3.4%
Group 577
 
0.5%

Length

2024-12-09T14:22:23.099707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:23.240885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
transient 89613
75.1%
transient-party 25124
 
21.0%
contract 4076
 
3.4%
group 577
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1218870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1218870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1218870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 233550
19.2%
t 148013
12.1%
r 144514
11.9%
a 143937
11.8%
T 114737
9.4%
s 114737
9.4%
i 114737
9.4%
e 114737
9.4%
y 25124
 
2.1%
- 25124
 
2.1%
Other values (7) 39660
 
3.3%

adr
Real number (ℝ)

ZEROS 

Distinct8879
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.83112
Minimum-6.38
Maximum5400
Zeros1959
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size932.9 KiB
2024-12-09T14:22:23.409546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile38.4
Q169.29
median94.575
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.71

Descriptive statistics

Standard deviation50.53579
Coefficient of variation (CV)0.49627059
Kurtosis1013.1899
Mean101.83112
Median Absolute Deviation (MAD)27.825
Skewness10.530214
Sum12157618
Variance2553.8661
MonotonicityNot monotonic
2024-12-09T14:22:23.621051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3754
 
3.1%
75 2715
 
2.3%
90 2473
 
2.1%
65 2418
 
2.0%
0 1959
 
1.6%
80 1889
 
1.6%
95 1661
 
1.4%
120 1607
 
1.3%
100 1573
 
1.3%
85 1538
 
1.3%
Other values (8869) 97803
81.9%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 1959
1.6%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 15
 
< 0.1%
1.29 1
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 1
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%

required_car_parking_spaces
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
0
111974 
1
 
7383
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2024-12-09T14:22:23.794116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:24.002369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 119390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111974
93.8%
1 7383
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57136276
Minimum0
Maximum5
Zeros70318
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size932.9 KiB
2024-12-09T14:22:24.115699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79279842
Coefficient of variation (CV)1.387557
Kurtosis1.4925648
Mean0.57136276
Median Absolute Deviation (MAD)0
Skewness1.3491894
Sum68215
Variance0.62852934
MonotonicityNot monotonic
2024-12-09T14:22:24.258869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 70318
58.9%
1 33226
27.8%
2 12969
 
10.9%
3 2497
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
0 70318
58.9%
1 33226
27.8%
2 12969
 
10.9%
3 2497
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
5 40
 
< 0.1%
4 340
 
0.3%
3 2497
 
2.1%
2 12969
 
10.9%
1 33226
27.8%
0 70318
58.9%

reservation_status
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Check-Out
75166 
Canceled
43017 
No-Show
 
1207

Length

Max length9
Median length9
Mean length8.619474
Min length7

Characters and Unicode

Total characters1029079
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 75166
63.0%
Canceled 43017
36.0%
No-Show 1207
 
1.0%

Length

2024-12-09T14:22:24.430455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T14:22:24.592563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
check-out 75166
63.0%
canceled 43017
36.0%
no-show 1207
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1029079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1029079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1029079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 161200
15.7%
C 118183
11.5%
c 118183
11.5%
h 76373
7.4%
- 76373
7.4%
u 75166
7.3%
t 75166
7.3%
O 75166
7.3%
k 75166
7.3%
a 43017
 
4.2%
Other values (7) 135086
13.1%
Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size932.9 KiB
Minimum2014-10-17 00:00:00
Maximum2017-09-14 00:00:00
2024-12-09T14:22:24.756269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:24.969775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-12-09T14:22:09.578433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:41.360625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.590746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.439709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.735954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:50.192252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.514054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:54.821241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.792832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.127693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.012258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.165901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:05.145038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.464958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:09.763050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:41.516273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.730886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.588864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.900689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:50.389758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.671151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.053745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.937516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.264429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.156978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.299065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:05.309620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.623014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:09.925263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:41.722791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.858056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.729633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.045926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:50.566322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.806384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.198876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.082685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.403538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.289214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.433297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:05.466310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.749343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.096842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:41.863932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.998750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.881858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.203540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:50.744951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.969943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.355490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.235397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.544415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.473940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.691176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:05.640469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.872185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.250030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.057485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.137926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.087055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.347670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:50.909619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.141562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.507683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.412414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.676582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.700548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.811405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:05.834060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.021274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.398875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.246122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.260185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.256704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.533215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.057736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.284349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.649517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.627412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.808739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:01.872237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.954131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.048530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.155604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.542972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.406757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.378469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.419461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.676248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.212366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.443072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.772740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.803561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:59.928977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.018363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.090356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.231079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.280439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.685750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.566890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.500126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.576622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:48.878781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.375479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.622843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:55.894001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:57.973214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.053708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.173987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.221599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.393759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.414625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.850830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.736360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.632332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.844497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.069310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.537190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.812824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.034846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.248286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.177933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.324098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.367281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.567399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.548781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:10.993487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:42.881497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.750532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:46.974702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.222937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.673341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:53.963531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.151199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.378453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.298242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.495856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.494566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.704557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.683976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:11.158576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.009196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:44.860832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.117426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.362120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.803625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:54.100756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.263423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.502642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.425493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.623030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.588833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.847765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.825139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:11.331179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.149142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.019550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.261547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.510834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:51.951333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:54.268418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.387609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.651423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.550342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.747883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.706036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:06.984433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:08.964844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:11.495910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.314831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.172734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.433130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.681893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.135953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:54.490901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.528298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.824629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.704413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:02.901990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.840199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.151615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:09.210297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:11.632058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:43.450987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:45.306515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:47.590305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:49.901469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:52.262168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:54.672453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:56.656614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:21:58.969449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:00.842152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:03.034186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:04.979851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:07.297327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T14:22:09.413793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-09T14:22:25.127496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencompanycustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.280-0.0490.0270.0010.0740.0000.0000.0000.0050.0000.0520.000-0.0400.0070.0000.0000.0000.0000.0150.0000.000-0.143-0.1500.0000.0000.0000.0940.0510.196
adults0.2801.000-0.0560.0020.0100.0260.0150.0000.000-0.0850.0000.2300.089-0.0370.0000.0080.0140.0130.0000.1920.0080.000-0.210-0.0360.0000.0080.0030.1530.1270.162
agent-0.049-0.0561.0000.0050.083-0.0570.0910.1330.0260.0910.0580.2260.125-0.0190.1190.2090.8170.0860.076-0.1230.2220.1850.060-0.1680.1310.0640.1430.1700.1310.015
arrival_date_day_of_month0.0270.0020.0051.0000.0580.0610.0440.0090.0050.0120.0100.0460.0320.0320.0540.0280.0260.0210.0170.0080.0330.039-0.001-0.0120.0080.0230.010-0.016-0.0070.003
arrival_date_month0.0010.0100.0830.0581.0000.8010.4290.0270.0160.0100.0690.2170.1030.0600.1010.0690.0700.0700.0750.1320.0880.0890.0170.0320.0180.0650.0450.0370.0460.053
arrival_date_week_number0.0740.026-0.0570.0610.8011.0000.4240.0280.0140.0080.062-0.0580.106-0.0040.0950.0640.0670.0660.0750.1130.0810.080-0.0430.0870.0170.0610.0420.0260.0260.019
arrival_date_year0.0000.0150.0910.0440.4290.4241.0000.0530.0090.0160.0440.2810.2130.0740.0520.0270.0430.0260.0100.1040.1590.1120.0250.0520.0180.0230.0820.0140.0290.091
assigned_room_type0.0000.0000.1330.0090.0270.0280.0531.0000.0440.0510.3040.0850.0900.0290.1920.0950.3910.2030.0710.0620.1210.1160.0030.0080.0920.1450.7760.0470.0510.066
babies0.0000.0000.0260.0050.0160.0140.0090.0441.0000.0170.0250.0320.0150.0000.0230.0290.0490.0340.0070.0070.0340.0150.0000.0000.0200.0240.0400.0000.0100.060
booking_changes0.005-0.0850.0910.0120.0100.0080.0160.0510.0171.0000.0180.1760.028-0.0190.0290.0270.0400.0480.000-0.0080.0200.0110.031-0.0730.0160.0340.0140.0640.0400.042
children0.0000.0000.0580.0100.0690.0620.0440.3040.0250.0181.0000.0390.0610.0180.0730.0430.0460.0280.0350.0280.1000.0370.0020.0000.0300.0280.3570.0130.0280.061
company0.0520.2300.2260.0460.217-0.0580.2810.0850.0320.1760.0391.0000.2510.0210.1840.2180.4980.1420.3580.2860.3930.200-0.298-0.1980.0480.1060.0980.2500.076-0.128
customer_type0.0000.0890.1250.0320.1030.1060.2130.0900.0150.0280.0610.2511.0000.0780.0980.0790.0520.1360.1050.1220.2760.1390.0140.0090.0410.0970.1090.0800.0880.097
days_in_waiting_list-0.040-0.037-0.0190.0320.060-0.0040.0740.0290.000-0.0190.0180.0210.0781.0000.1270.0270.0870.0680.0240.1530.0780.062-0.0190.1160.0340.0500.0280.012-0.075-0.123
deposit_type0.0070.0000.1190.0540.1010.0950.0520.1920.0230.0290.0730.1840.0980.1271.0000.0910.1770.4810.0580.2730.3740.0930.0130.0510.0710.3470.1520.0470.0730.220
distribution_channel0.0000.0080.2090.0280.0690.0640.0270.0950.0290.0270.0430.2180.0790.0270.0911.0000.1870.1770.2970.1160.6920.0770.1080.0510.0760.1290.1000.0060.0550.070
hotel0.0000.0140.8170.0260.0700.0670.0430.3910.0490.0400.0460.4980.0520.0870.1770.1871.0000.1360.0500.0940.1470.3170.0170.0500.2210.1360.3230.1920.1980.046
is_canceled0.0000.0130.0860.0210.0700.0660.0260.2030.0340.0480.0280.1420.1360.0680.4810.1770.1361.0000.0850.2810.2670.0500.0410.0440.1971.0000.0730.0280.0220.265
is_repeated_guest0.0000.0000.0760.0170.0750.0750.0100.0710.0070.0000.0350.3580.1050.0240.0580.2970.0500.0851.0000.1340.3470.0600.3200.1850.0780.0860.0370.0170.0820.040
lead_time0.0150.192-0.1230.0080.1320.1130.1040.0620.007-0.0080.0280.2860.1220.1530.2730.1160.0940.2810.1341.0000.1700.089-0.1890.1710.0570.2070.0480.2960.162-0.074
market_segment0.0000.0080.2220.0330.0880.0810.1590.1210.0340.0200.1000.3930.2760.0780.3740.6920.1470.2670.3470.1701.0000.1910.0970.0540.0920.1950.1380.0330.0610.210
meal0.0000.0000.1850.0390.0890.0800.1120.1160.0150.0110.0370.2000.1390.0620.0930.0770.3170.0500.0600.0890.1911.0000.0140.0880.0270.0400.1030.0450.0610.062
previous_bookings_not_canceled-0.143-0.2100.060-0.0010.017-0.0430.0250.0030.0000.0310.002-0.2980.014-0.0190.0130.1080.0170.0410.320-0.1890.0970.0141.0000.1020.0190.0290.003-0.119-0.0840.025
previous_cancellations-0.150-0.036-0.168-0.0120.0320.0870.0520.0080.000-0.0730.000-0.1980.0090.1160.0510.0510.0500.0440.1850.1710.0540.0880.1021.0000.0000.0310.006-0.062-0.055-0.129
required_car_parking_spaces0.0000.0000.1310.0080.0180.0170.0180.0920.0200.0160.0300.0480.0410.0340.0710.0760.2210.1970.0780.0570.0920.0270.0190.0001.0000.1390.0790.0170.0150.044
reservation_status0.0000.0080.0640.0230.0650.0610.0230.1450.0240.0340.0280.1060.0970.0500.3470.1290.1361.0000.0860.2070.1950.0400.0290.0310.1391.0000.0520.0300.0240.189
reserved_room_type0.0000.0030.1430.0100.0450.0420.0820.7760.0400.0140.3570.0980.1090.0280.1520.1000.3230.0730.0370.0480.1380.1030.0030.0060.0790.0521.0000.0440.0540.075
stays_in_week_nights0.0940.1530.170-0.0160.0370.0260.0140.0470.0000.0640.0130.2500.0800.0120.0470.0060.1920.0280.0170.2960.0330.045-0.119-0.0620.0170.0300.0441.0000.2380.076
stays_in_weekend_nights0.0510.1270.131-0.0070.0460.0260.0290.0510.0100.0400.0280.0760.088-0.0750.0730.0550.1980.0220.0820.1620.0610.061-0.084-0.0550.0150.0240.0540.2381.0000.079
total_of_special_requests0.1960.1620.0150.0030.0530.0190.0910.0660.0600.0420.061-0.1280.097-0.1230.2200.0700.0460.2650.040-0.0740.2100.0620.025-0.1290.0440.1890.0750.0760.0791.000

Missing values

2024-12-09T14:22:11.910933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-09T14:22:12.676498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-09T14:22:13.395516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No DepositNaNNaN0Transient0.000Check-Out2015-07-01
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No DepositNaNNaN0Transient0.000Check-Out2015-07-01
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No DepositNaNNaN0Transient75.000Check-Out2015-07-02
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.0NaN0Transient75.000Check-Out2015-07-02
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
5Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
6Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No DepositNaNNaN0Transient107.000Check-Out2015-07-03
7Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303.0NaN0Transient103.001Check-Out2015-07-03
8Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0Transient82.001Canceled2015-05-06
9Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.0NaN0Transient105.500Canceled2015-04-22
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
119380City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient140.7501Check-Out2017-09-04
119381City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14.0NaN0Transient99.0000Check-Out2017-09-05
119382City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7.0NaN0Transient209.0000Check-Out2017-09-05
119383City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No Deposit42.0NaN0Transient87.6000Check-Out2017-09-06
119384City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.0NaN0Transient96.1402Check-Out2017-09-06
119385City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.0NaN0Transient96.1400Check-Out2017-09-06
119386City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9.0NaN0Transient225.4302Check-Out2017-09-07
119387City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9.0NaN0Transient157.7104Check-Out2017-09-07
119388City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No Deposit89.0NaN0Transient104.4000Check-Out2017-09-07
119389City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient151.2002Check-Out2017-09-07

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
5407City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled2016-04-04180
4183City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.000Canceled2016-01-06150
5077City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled2016-01-18109
4881City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN31Transient130.000Canceled2016-01-18101
3852City Hotel1342015December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled2015-11-17100
3794City Hotel1282017March920320.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient95.000Canceled2017-02-0299
3908City Hotel1382017January2140110.00BBPRTCorporateCorporate000AA0Non RefundNaN67.00Transient75.000Canceled2016-12-0799
4874City Hotel11562017April17260320.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN0Transient100.000Canceled2016-11-2199
4207City Hotel1712016June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient120.000Canceled2016-04-2789
4941City Hotel11662016November4510310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient110.000Canceled2016-07-1385